READING

Saffari et al. propose an online random forest algorithm (see [1] for the original paper introducing random forests) based on online bagging [2]. Note the difference between on-line and incremental algorithms: While incremental algorithms have memory, that is they may store each incoming sample, an online algorithm sees each sample exactly once. On common datasets, as for example the USPS Dataset, Saffari et al. show that online random forests converge to the offline algorithm.

The original C++ implementation of online random forests can be found on Saffari's webpage. Figure 1, in contrast, shows the performance of a custom implementation on the MNIST dataset [3].